Pretend you are the general manager of the Browns, and you have to decide whether Tramon Williams is good enough to be a starting corner on a good defense or if you need to upgrade that position in the offseason. In front of you sits a decent amount of information in the form of evaluations. These are the evaluations of your coaches and scouts who have worked with Williams and know him well. They have coached him, reviewed tape, studied him and written down their thoughts in a systematic way.
But you're curious. You have questions:
Is there a way to know how often receivers got open against him, and compare that to all the other starting corners in the league, based on every play of every game?
Did he show significant improvement during the season in an area of weakness in these evaluations? Maybe he allowed receivers to get open on deep crossing routes, but began to improve. Maybe his trajectory is improving, but coaches have specific moments of failure burned into their brains.
If Williams isn't good enough to be a starting corner, could he be a low-priced backup who at least knows our scheme? What is the value of familiarity?
And what do we do with this new chip technology the NFL is handing us? (More on that later.)
Those are the kinds of questions someone who understands the power of analytics might help answer. It's not pure numbers -- after all, it's all born out of watching Williams' every play -- but it demands that observations turn into data. It's also why someone with no real NFL experience but a wealth of experience dealing with gaining a competitive advantage for teams through data could be of great use. That someone is a guy like Paul DePodesta. He has the potential to help an NFL team gain a real competitive advantage as the NFL moves into a new world of data. And it doesn't have to "disrupt" the product in the way some might think.
DePodesta was made famous by Michael Lewis' "Moneyball" due to his ability to turn baseball statistics into information that helped the Oakland A's compete against big-spending teams like the New York Yankees for a fraction of the total salary. He is now the chief strategy officer for the Cleveland Browns, a hire that has inspired much hand-wringing in some corners. Some folks believe DePodesta has not earned a position of power in an NFL franchise because of his lack of experience and knowledge of the NFL. But to paraphrase what an NBA executive once told me after I was hired to be an analytics consultant, DePodesta wasn't hired for his knowledge of football. Instead he was hired for his expertise in taking massive amounts of data and providing information the football experts can utilize to make better decisions. He is not there to reinvent football; he is there to give football people more tools. And he is there to drive that kind of thinking throughout the entire organization.
To be clear, DePodesta will not be doing a ton of number-crunching for the Browns. The Browns already have Ken Kovash on staff; he is one of the top analysts in the NFL and will lead the actual analysis. But NFL teams don't just already have a lot of useful data, they are also about to be caught under an avalanche of new data. The league has installed chips in every player's shoulder pads and can now track a player's location and movements multiple times a second during every play. The teams will be given access to that data soon, and they need to have some way of turning that massive data set into useful information to gain a competitive advantage.
That, or they'll be left behind.
This is where DePodesta's experience is exactly what teams need. People who have spent their lives in the NFL have a ton of valuable experience and knowledge, and DePodesta (or someone like him) cannot replace that. Those same football people though, have little to no experience generating a competitive advantage out of a massive new data set. What DePodesta has that many (if not all) football lifers do not is the experience of effectively turning massive amounts of data into information that helps those football people make better decisions in every part of the organization. This kind of data can be utilized to assist the GM with making better personnel decisions, give the coach even more insight into opponents' strengths and weaknesses when developing game plans, and help position coaches better track and understand the progress their players are making.
And, given all the new kinds of data teams will be offered, people like DePodesta can be valuable in figuring out not just what should be used, but what should NOT be used.
There is no guarantee that DePodesta will be successful in his new position. After all, if the Browns don't have a good quarterback, they could have the minds behind the Manhattan Project in the front office and it wouldn't matter. He is, however, imminently qualified to help a sports organization learn the best way to deal with data.
Ben Alamar is the Director of Production Analytics at ESPN.